innovative approaches to spectrum selection, sensing, and sharing in cognitive radio networks

U
UNIVERSITY OF CINCINNATI
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05.06.09
Chittabrata Ghosh
Doctor in Philosophy (Ph.D.)
Computer Science and Engineering
Innovative Approaches to Spectrum Selection, Sensing, and
Sharing in Cognitive Radio Networks
Chittabrata Ghosh
Prof. Dharma P. Agrawal
Prof. Raj Bhatnagar
Prof. Chia-Yung Han
Prof. Yiming Hu
Prof. Marepalli B. Rao
Prof. Dharma P. Agrawal

Innovative Approaches to Spectrum Selection, Sensing,
and Sharing in Cognitive Radio Networks
by
Chittabrata Ghosh
B.Tech. (Kalyani University, India) 2000
M.S. (Indian Institute of Technology (I.I.T) Kharagpur, India) 2004
A dissertation submitted in partial satisfaction of the
requirements for the degree of
Doctor of Philosophy
in
Computer Science and Engineering
in the
Department of Computer Science
of the
College of Engineering
of the
UNIVERSITY OF CINCINNATI, OHIO
Committee:
Professor Dharma P. Agrawal, Chair
Professor Raj Bhatnagar
Professor Chia-Yung Han
Professor Yiming Hu
Professor M. Bhaskara Rao
May 2009
Abstract
Innovative Approaches to Spectrum Selection, Sensing, and Sharing in
Cognitive Radio Networks
In a cognitive radio network (CRN), bands of a spectrum are shared by licensed (primary)
and unlicensed (secondary) users in that preferential order. It is generally recognized that
the spectral occupancy by primary users exhibit dynamical spatial and temporal properties.

In the open literature, there exist no accurate time-varying model representing the spectrum
occupancy that the wireless researchers could employ for evaluating new algorithms and
techniques designed for dynamic spectrum access (DSA). We use statistical characteristics
from actual radio frequency measurements, obtain first- and second-order parameters, and
define a statistical spectrum occupancy model based on a combination of several different
probability density functions (PDFs).
One of the fundamental issues in analyzing spectrum occupancy is to characterize it in
terms of probabilities and study probabilistic distributions over the spectrum. To reduce
computational complexity of the exact distribution of total number of free bands, we resort
to efficient approximation techniques. Furthermore, we characterize free bands into five
different types based on the occupancy of its adjacent bands. The probability distribution
of total number of each type of bands is therefore determined. Two corresponding algo-
rithms are effectively developed to compute the distributions, and our extensive simulation
results show the effectiveness of the proposed analytical model.
Design of an efficient spectrum sensing scheme is a challenging task, especially when false
alarms and misdetections are present. The status of the band is to be monitored over a num-
ber of consecutive time periods, with each time period being of a specific time interval. The
status of the sub-band at any time point is either free or busy. We proved that the status
of the band over time evolves randomly, following a Markov chain. The cognitive radio
assesses the band, whether or not it is free, and the assessment is prone to errors. The errors
are modeled probabilistically and the entire edifice is brought under a hidden Markov chain
model in predicting the true status of the band.
3
After spectrum sensing, our research direction is on spectrum sharing using cooperative
communication. We discuss allocation strategies of unused bands among the cognitive
users. We introduce a cooperative N-person Game among the N cognitive users in a CRN
and then identify strategies that help achieve Nash equilibrium. When licensed users arrive
in any of those sub-bands involved in unlicensed user communication, the affected cogni-
tive users in those bands remove them out of the N-person game and assess their optional
strategies with the licensed users using the 2-person game approach for coexistence with

the licensed users. In the sequel of spectrum sharing, we present three novel priority-based
spectrum allocation techniques for enabling dynamic spectrum access (DSA) networks em-
ploying non-contiguous orthogonal frequency division multiplexing (NC-OFDM) trans-
mission.
The allocation of bandwidth to unlicensed users, without significantly increasing the inter-
ference on the existing licensed users, is a challenge for Ultra Wideband (UWB) networks.
We propose a novel Rake Optimization and Power Aware Scheduling (ROPAS) architec-
ture for UWB networks as multipath diversity in UWB communication encourages us to
use a Rake receiver.
4
To
Dad and Mom for having confidence in me,
and
my sister and wife, Suprita for their continuous inspiration and support
Acknowledgments
I would like to express my sincere gratitude and specially “thank” my advisor, Dr. Dharma
P. Agrawal for his unflinching support and continuous motivation for quality work. His
momentum and spontaneity has always restored an aura of research excellence at the Center
of Distributed and Mobile Computing (CDMC). Dr. Agrawal gave me the freedom to
pursue inter-disciplinary research and encouraged me to publish and attend conferences to
acquire knowledge that helped me in building a solid foundation in the area of Cognitive
Radio Networks. For all his help, I am really indebted to him.
I would also like to extend my thanks to Dr. M. B. Rao for imparting valuable knowl-
edge to me in the domain of Game Theory, Probability, and Statistics. This knowledge has
become an integral part of my research work. I wold also like to thank Dr. Bhatnagar, not
only for his help in the academics and administrative issues, but also for his kind support
for our Computer Science Graduate Student Association. Dr. Berman and Dr. Han were
really instrumental in providing subtle advices that accelerated my research work. I am
indebted to all of them for acceeding to be members of my thesis committee.
Special thanks owes to Dr. A. M. Wyglinski for the enriched collaborative work on de-

signing the innovative spectrum occupancy model. The real-time measurements performed
by his students turned out to be an asset and invaluable for my research work.
I would also like to thank my colleagues at the CDMC laboratory as they have always
been a source of inspiration and create a positive research atmosphere all the time. I would
like to specially thank Bing, Junfang, Yun, Deepti, Asitha, Weihuang, Demin, Peter and
Kuheli for their personal and professional assistance on myriad occasions. Special thanks
to Dr. Bin Xie for teaching me the process of technical writing.
Last but not the least, I would take this opportunity to thank by dearest parents. Without
their dream, I would not have sailed so far in life. My heartiest thanks to my sister, who
is so very caring and loving all throughout my life. Special thanks to my wife, Suprita for
always being so supportive and motivating me during my doctoral research.
i
Contents
List of Figures v
List of Tables ix
1 Introduction 2
1.1 Motivation . 6
1.2 Organization of the Thesis . 7
1.2.1 Chapter 2: A Framework for Statistical Wireless Spectrum Occu-
pancy Modeling . . . 8
1.2.2 Chapter 3: Probabilistic Approach to Spectrum Occupancy . 8
1.2.3 Chapter 4: Hidden Markov Model in Spectrum Sensing . 8
1.2.4 Chapter 5: Game Theoretic Approach in Spectrum Sharing . 9
1.2.5 Chapter 6: Priority-based Spectrum Allocation in Cognitive Radio
Networks Employing NC-OFDM Transmission . 9
1.2.6 Chapter 7: Cross-Layer Architecture for Joint Power and Link Op-
timization . 10
1.2.7 Chapter 8: Conclusions and Future Work . 11
2 A Framework for Statistical Wireless Spectrum Occupancy Modeling 12
2.1 Introduction . . . 12

2.2 Real-time Data Measurements . . 15
2.2.1 USRP Measurements . 15
2.2.2 Paging-band Measurements . 16
2.3 Proposed Spectrum Occupancy Model 17
2.3.1 Statistical Analysis of Spectrum Occupancy 18
2.3.2 Proposed Spectrum Occupancy Model Implementation . 20
2.4 M/M/1 Queuing Model Representation of Spectrum Occupancy 22
2.4.1 Case Study Using Real Time Measurements 24
2.5 Performance Evaluation . 26
2.5.1 Time Slice Validation . 27
2.5.2 Frequency Slice Validation . 29
ii
2.6 Conclusion . . . 32
3 Probabilistic Approach to Spectrum Occupancy 33
3.1 Introduction . . . 33
3.2 Related Work . 36
3.3 System Model and Problem Formulation . 38
3.3.1 Sub-band Free Probability Model . 38
3.3.2 Probability Distribution of N
free
40
3.3.3 Approximation with Normal Distribution . 42
3.4 Probability Distribution of N
free
45
3.4.1 Approximate Distribution of N
free
small
46
3.4.2 Approximate Distribution of N

free
mod
47
3.4.3 Approximate Distribution of N
free
large
47
3.4.4 Approximate Distribution of N
free
48
3.5 Neighborhood Occupancy of Free Sub-bands 50
3.5.1 Sub-band Types . 50
3.5.2 Probability Distribution of X
I
(N) 52
3.5.3 Probability Distribution of X
II
(N) 56
3.5.4 Probability Distribution of X
III
(N) 57
3.5.5 Probability Distribution of X
IV
(N) 58
3.5.6 Probability Distribution of X
V
(N) 58
3.6 Implementation and Performance Evaluation . . . 59
3.6.1 Algorithm for Probability Distribution of N
free

60
3.6.2 Algorithm for Probability Distribution of X
I
(N) 62
3.6.3 Simulation Configuration 63
3.6.4 Distribution of N
free
64
3.6.5 Computational Efficiency . 65
3.6.6 Probability Distribution of X
i
(N) 66
3.6.7 Statistical Analysis of X
I
(N) 70
3.6.8 Special Case (p
i
= p
j
) 71
3.7 Conclusion . . . 72
4 Hidden Markov Model in Spectrum Sensing 73
4.1 Introduction . . . 73
4.2 Related Work on Spectrum Sensing . 75
4.3 System Model and Problem Formulation . 76
4.4 Markov Chain Modeling of True States and its Validation . . . 78
4.4.1 Markov Chain Assumption Validation 78
4.5 HMM Parameter Estimation . 80
4.6 Viterbi Algorithm and the Expectation Maximization Algorithm 85
4.6.1 Viterbi-based Sensing Algorithm . 85

4.6.2 Expectation Maximization Algorithm 87
iii
4.7 Hidden Markov Model in Spectrum Sensing . . 89
4.8 Validation and Simulation Results . 91
4.9 Conclusion . . . 98
5 Game Theoretic Approach in Spectrum Sharing 99
5.1 Introduction . . . 99
5.2 Related Work . 101
5.3 Spectrum Model and Basic Components of Spectrum Sharing . 103
5.4 Channel Capacity Optimization and Game Theoretic Formulation . 111
5.4.1 Channel Capacity Optimization . 112
5.4.2 Optimization, Game Theory, and Nash Equilibrium 112
5.4.3 Case Study 115
5.5 Game Theoretic Perspective using Spectrum Sensing Parameters 119
5.5.1 Case Study 120
5.6 Experimental Results . 124
5.7 2-Person Game Formulation for Coexistence of PUs and SUs . 128
5.8 Conclusion . . . 131
6 Priority-based Spectrum Allocation for Cognitive Radio Networks Employing
NC-OFDM Transmission 133
6.1 Introduction . . . 133
6.2 System Model . 135
6.2.1 Wireless Multicarrier Transmission Format 137
6.3 Proposed Priority-based Spectrum Allocation Techniques . . . 138
6.3.1 First Available First Allocate (FAFA) Spectrum Allocation Approach139
6.3.2 Best Available Selective Allocate (BASA) Spectrum Allocation Ap-
proach 140
6.3.3 Best Available Multiple Allocate (BAMA) Spectrum Allocation
Approach . 143
6.4 Simulation Results 144

6.4.1 Computation of Priority Metrics from Real-time Measurements . . 145
6.4.2 Comparative Analysis of Proposed Algorithms . 146
6.5 Conclusion . . . 153
7 Cross-layer Architecture for Joint Power and Link Allocation 154
7.1 Introduction . . . 154
7.2 Related Work . 156
7.3 The ROPAS Architecture . . . 158
7.3.1 Rake Optimization Module . 162
7.3.2 Interference Measurement (IM) . 166
7.3.3 Channel Estimation Block (CEB) . 167
7.3.4 Channel Scanner . . .
168
iv
7.3.5 Power Aware Scheduling 168
7.4 Priority Based Scheduling . 171
7.5 Simulation Results 173
7.5.1 Multi-objective Rake Optimization . . . 174
7.5.2 Power Aware Scheduling in ROPAS . . 176
7.5.3 Priority based Joint link and Power Scheduling . 177
7.6 Conclusion . . . 178
8 Conclusions and Future Work 180
8.0.1 Future Work . 182
Bibliography 188
v
List of Figures
1.1 Advancement of technology and signal processing leading towards re-configurable
SDRs. 3
1.2 Evolution of Software Defined Radio. . . . 4
1.3 Technological evolution from SDR to AI-SR. 5
1.4 Various functionalities of a CR. . . 7

2.1 Snapshot of spectrum utilization (700-800 MHz) over an 18 hour period in
Hoboken, New Jersey [4]. The shaded regions indicate primary user access
while the white spaces imply no primary user activity. . 12
2.2 Measured power spectrum obtained in the paging band (928-968 MHz).
The measurement setup was located at Global Positioning System (GPS)
latitude 42
◦
16

24.94

N and longitude 71
◦
48

35.29

W. During the mea-
surement campaign, 500 scans or sweeps were conducted between 3:31 -
4:30 PM with frequency resolution of 20 KHz. . 16
2.3 Measured power spectrum obtained in the paging band (928-968 MHz).
The measurement setup was located at Global Positioning System (GPS)
latitude 42
◦
16

24.94

N and longitude 71
◦

48

35.29

W. During the mea-
surement campaign, 1500 scans or sweeps were conducted between 3:31 -
7:30 PM with frequency resolution of 5 KHz. 17
2.4 Queuing model representation of sub-band utilization by the BS. 22
2.5 Probability of time (wait and service) for the SUs with varying idle dura-
tions. The average service time for each SU is assumed to be 2 s and arrival
rate of SUs into the queue is assumed to be 0.25. 25
2.6 Probability of waiting time in the queue for the SUs with varying idle dura-
tions. The average service time for each SU is assumed to be 2 s and arrival
rate of SUs into the queue is assumed to be 0.25. 25
2.7 Comparison of CCDF plot against percentage ON time between model out-
put and real-time measurements with threshold set to (µ + σ) and (µ + 3σ).
CCDF plot against percentage ON time over 250 time sweeps. The training
of our model is performed on the first 250 time sweeps. . 27
vi
2.8 Comparison of CCDF plot against percentage ON time between model out-
put and real-time measurements with threshold set to (µ + σ) and (µ + 3σ).
CCDF plot against percentage ON time over 500 time sweeps. The training
of our model is performed on the first 1000 time sweeps. . . . 28
2.9 Comparison of CCDF plot against percentage ON time between model out-
put and USRP measurements with threshold set to (µ + σ) and (µ + 3σ). . . 29
2.10 Percentage of bandwidth occupied over 250 time sweeps. The variation
in bandwidth occupancy is studied using threshold values (µ + σ). This
comparison is performed using the real-time measurements. . 30
2.11 Percentage of bandwidth occupied over 250 time sweeps. The variation
in bandwidth occupancy is studied using threshold values (µ + 3σ). This

comparison is performed using the real-time measurements. . 30
2.12 Variation in total bandwidth occupied over the period of our experiment
conducted for threshold values ranging from µ+σ to µ+10σ with n varying
between 1 and 10 with step size of 0.5 31
3.1 Spectrum utilization (446-740 MHz) by television broadcasting in Cincin-
nati, Ohio, USA . 34
3.2 Spectrum occupancy of N sub-bands by primary users at time instant t 40
3.3 Configuration of probabilities in a spectrum of (a) 16 sub-bands and (b) 30
sub-bands . 41
3.4 Types of free sub-bands: (a) Type I, (b) Type II, (c) Type III, (d) Type IV,
and(e)TypeV 51
3.5 (m + 1)-spectrum derived from m-spectrum for Type I sub-bands 53
3.6 Exact distribution of N
free
and its normal and Poisson-normal approxima-
tions for 16 sub-bands with 1 small and 6 large sub-band free probabilities . 64
3.7 Exact distribution of N
free
and its normal and Poisson-normal approxima-
tions for 30 sub-bands with (a) 5 small and 5 large sub-band free probabil-
ities and (b) 2 small and 16 large sub-band free probabilities . 65
3.8 Comparison of probability distributions of N
free
and X
i
(N), i = I, II, III in
a spectrum of 16 sub-bands . 68
3.9 Comparison of probability distributions of N
free
and X

i
(N), i = I, II, III in a
spectrum of 30 sub-bands with 5 small and 5 large sub-band free probabilities 69
3.10 Comparison of probability distributions of N
free
and X
i
(N), i = I, II, III
in a spectrum of 30 sub-bands with 2 small and 16 large sub-band free
probabilities . . . 69
4.1 The system model implemented for enhanced spectrum sensing. 77
4.2 Power measurements obtained from paging bands over 500 time periods. . . 79
4.3 Estimation accuracy of our Markov chain model over paging bands for 99
observation periods performed over 1000 iterations. 81
4.4 Hidden Markov model representation in spectrum sensing . . . 82
vii
4.5 Expectation-maximization algorithm for estimating parameter values . . . . 91
4.6 Frequency distribution of prediction accuracy percentage of the Viterbi al-
gorithm with mis-detection probability (Pmd) δ = 0.05 and false alarm
probability (Pfa)  specified in the inset of each histogram (Case I, Sce-
nario 1) . 93
4.7 Frequency distribution of prediction accuracy percentage of the Viterbi al-
gorithm with  = 0.05 and δ specified in the inset of each histogram (Case
I, Scenario 2) 94
4.8 Frequency distribution of prediction accuracy percentage of the Viterbi al-
gorithm with  = 0.05 and δ specified in the inset of each histogram (Case
II, Scenario 2) 94
4.9 Frequency distribution of prediction accuracy percentage of the Viterbi al-
gorithm with  = 0.05 and δ specified in the inset of each histogram (Case
III, Scenario 2) . 95

4.10 Normal approximation of the Viterbi algorithm for Case I with δ = 0.05
and  specified in the inset . 96
4.11 Normal approximation of the Viterbi algorithm for Case I with  = 0.05
and δ specified in the inset . 96
5.1 Distribution of PUs and SUs in one particular cell . . 103
5.2 Type classifications of various configurations of free channels . 110
5.3 Free channel configurations for (a) SU
1
and (b) SU
2
115
5.4 Transmission power variation over 10 frequency slots, i.e., 928.8 MHz to
929 MHz. . 125
5.5 Transmission power variation over 10 frequency slots, i.e., 929 MHz to
929.20 MHz. 125
5.6 Idle durations over 10 consecutive idle intervals in both paging bands, i.e.,
928.8 MHz to 929 MHz and 929 MHz to 929.20 MHz. . 126
5.7 Quality of channels over sweeps 80 to 100 based on their neighboring chan-
nels considered for both paging bands, i.e., 928.8 MHz to 929 MHz and 929
MHz to 929.20 MHz. . 127
5.8 SNR computations based on our reward function defined in Eq. 5.17 for
both paging bands, i.e., 928.8 MHz to 929 MHz and 929 MHz to 929.20
MHz. 128
5.9 SNR computations for SU 1 and SU 2 based on their utility functions de-
fined in Eq. 5.24 for paging bands 929 MHz to 929.20 MHz. . 129
6.1 Schematic diagram of the system model used for proposed priority schedul-
ing techniques among SUs. . 136
6.2 Flow diagram of the proposed FAFA approach. . 139
6.3 Flow diagram of the proposed BASA approach. . 140
6.4 Flow diagram of the proposed BAMA approach. . 142

viii
6.5 Proportion of active sub-carriers for NC-OFDM for all sub-bands for ten
time instants. . 145
6.6 BER of all the sub-bands in the spectrum for all ten time instants of our
simulation. . . . 147
6.7 Comparison between FAFA, BASA and BAMA schemes for the number of
un-allocated requests per time instant for increasing PU occupancy. . 148
6.8 Comparison between FAFA, BASA and BAMA schemes for the number of
un-allocated requests per time instant for increasing SU requests. 148
6.9 Aggregate bandwidth utilization in BASA, and BAMA schemes for vary-
ing number of SU requests. . 150
6.10 Aggregate throughput achieved in BAMA scheme for varying number of
SU requests. . . . 151
6.11 labelInTOC . . . 152
7.1 Cross-layer design of the ROPAS architecture 159
7.2 Pseudo code for the Rake multi-objective optimization . 165
7.3 Channel assignment based on the “free” channels detected by the IM in the
UWB (3.1-10.6 GHz) . 167
7.4 Sub-band division into multiple frmaes in Power Aware Scheduling illus-
trated in UWB . 169
7.5 (a) Values of Lagrange multiplier’s, λ

i
s for all 10 paths and (b) Strategic
selection of propagation paths based on BER values by our optimization
algorithm when path P1 is already selected . 174
7.6 Strategic selection of paths for optimal BER . . . 175
7.7 Reduction of BER with increase in iteration of path selection . 176
7.8 Magnitude of power vectors allocated in each subframe with unit frame
interval . 177

7.9 Magnitude of power vectors allocated in each subframe with frame inter-
val=2units . 179
7.10 Power allocations for each application request in 6 time slots . 179
7.11 Number of slots assigned per frame for varying values of BER . 179
ix
List of Tables
3.1 Notation . 39
3.2 Comparison between Normal approximation and exact distribution with
(k) for 16 sub-bands . 41
3.3 Comparison between Normal approximation and exact distribution with
(k) for 30 sub-bands . 42
3.4 Boundary conditions for Type I, II, and III sub-bands for X
i
(N)atN=3 . . 52
3.5 Computational efficiency comparison among the exact distribution and its
normal and Poisson-normal approximations . . 66
3.6 Probability distribution of N
free
and X
i
(N) depicted in Figure 3.9 68
3.7 Probability distribution of N
free
and X
i
(N) depicted in Figure 3.10 . 68
3.8 [Mean ± r ∗SD] intervals and probability of intervals . 69
3.9 Probability distribution of N
free
and X

i
(N) 70
3.10 Binomial distribution of N
free
and X
i
(N) 71
4.1 Statistical parameters of Estimation . 80
4.2 Emission Probability for Spectrum Sensing . . 90
4.3 Estimation accuracy for the EM algorithm . 98
5.1 Reward table to achieve Nash Equilibria . 118
5.2 Reward table to achieve Nash Equilibria with 10 strategies for SU1 and 6
for SU2 123
5.3 Reward table to achieve Nash Equilibria with 10 strategies for SU1 and
remaining 3 for SU2 123
5.4 Pay-off table to achieve Nash Equilibria . 130
1
Abbreviations used
BER Bit Error Rate
CCDF Complementary Cumulative Distribution Function
CR Cognitive Radio
CRN Cognitive Radio Network
DSA Dynamic Spectrum Access
HMM Hidden Markov Model
IF Intermediate Frequency
NC − OFDMA Non −contiguous Orthogonal Frequency Division Multiple Access
PDF Probability Density Function
PFA Probability of False Alarm
PMD Probability of Mis − detection
PU Primary User

RF Radio Frequency
ROPAS Rake Optimization and Power Aware Scheduling
SDR Software Defined Radio
SR Software Radio
SU Secondary User
USRP Universal Software Radio Peripheral
UWB Ultra Wideband
2
Chapter 1
Introduction
The generation of mobile communication started with the advent of Analog Mobile
Phone System back in the 1980’s. These first generation phones were based on the cellular
communication (using macro cells) and analog cellular technology. It took another decade
(around 1991) for the transition into the second generation which supports digital voice,
messaging and data services using macro, micro and pico cellular concepts. By 2001, the
third generation mobile devices hit the market with enhanced data communication services
and for the first time started supporting both narrowband and wideband multimedia ser-
vices.
With a rapid growth of wireless and mobile communication as well as wide acceptance
of the third generation mobile communication and beyond, integration and intercommuni-
cation of existing and future networks is not a far-sighted envision. In recent years, differ-
ent types of networks, like self-organizing ah hoc networks, wireless mesh networks, etc.
have rapidly evolved and exhibited much prospects in the wireless networking arena. The
ubiquitous, seamless access between second and third generation mobile communication,
broadband wireless access schemes, as well as inter-operation among the self organizing
networks encouraged the market to have a common terminal for different network entities.
To support universal access along with user satisfaction in terms of content, quality of ser-
vice (QoS), and cost, reconfigurable software radio (SR) [1]- [2], or its practical form,
software defined radio (SDR) terminals are indispensable. The need for additional band-
width for different wireless technologies has further increased the search for new spectrum

3
Second Generation
Phones: 1990-91
Third Generation
Phones: 2001
Re-configurable
Software Defined
Radio
Advances in signal processing and
technology impacts size reduction of
devices
Figure 1.1: Advancement of technology and signal processing leading towards re-
configurable SDRs.
and SDR is expected to provide a reasonable solution without any need to search for addi-
tional spectrum. The gradual transition from the first generation cellular communication to
the advent of re-configurable terminals and base stations is depicted in Figure 1.1.
The central idea of implementing reconfigurable network and terminal equipments is
to make international roaming services easy between different radio access networking
standards, diversification of applications and provide flexibility in switching between ap-
propriate radio access schemes. This intercommunication between multitude of networking
standards leads to the so called heterogeneous networks.
Before discussing depth of research topics, a brief introduction about the evolution of
SR from SDR is presented. Digital signal processing in any or all of the flexible functional
blocks as shown in Figure 1.2 defines the characteristics of a radio. Some of the versions
of the radios are defined for better appreciation of the evolution of SR from SDR based on
Figure 1.2.
SDR: It is defined as a radio where the digitization is performed at the baseband stage,
downstream from the receive antenna. This digitization is performed after the wideband
filtering at the radio frequency (RF) section, low noise amplification and passband filtering
at the intermediate frequency (IF) stage and down conversion of the signal to baseband

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Figure 1.2: Evolution of Software Defined Radio.
frequency. The reverse operations are valid for the transmit digitization.
Digital radio: It is defined as a radio where digitization of signal is performed at any
functional block between the antenna and the input/output (I/O) device as shown in Fig-
ure 1.2. A digital radio is not necessarily an SDR, if the signal processing after the A/D
converter block is performed by a special purpose, application-specific integrated circuit
(ASIC).
SR: It is defined as a modified version of SDR where the digitization of signal moves
from the baseband processing section to the IF and RF sections. This transition is possi-
ble in future with the development of faster signal processors, memory chips as well as
advancement in signal processing technology.
Adaptive Intelligent Software Radio (AI-SR) [1]- [2]: It is defined as a radio which
is capable of all functionalities in a SR as well as can adapt to its operational environment
for enhanced spectral efficiency and improved spectrum management.
The technological evolution of AI-SR from SDR is illustrated in Figure 1.3. As is

evident, the transition from SDR to SR is possible with the advent of efficient signal pro-
cessing techniques in conjunction with adept faster memory chips and signal processors
technologies. This enables digitization of a radio to move from the baseband signal section
all the way to IF and RF sections, making SR as a reality. Intelligent network algorithms
5
SDR
CR SR
++
Faster
Signal processors
Figure 1.3: Technological evolution from SDR to AI-SR.
need to be plugged in for such possible transition from SR to AI-SR, which in turn will
result in a higher spectral efficiency in a heterogeneous network environment.
The following are the two aspects of software functionality that may be incorporated
into a radio:
• Software processing of the transmitted or received signal; and
• Software control which implies intelligent adaptation of radio parameters with re-
spect to its environment.
Software signal processing is performed by a SDR since their operating frequencies and
waveforms are controlled by using various software. Switching between modulations and
protocols simply requires running different code by a special architecture called Cognitive
Radio (CR) [3], [4]. Hence, a CR adds intelligence into an SDR. The term “intelligence”
(also called intellect) is described in Wikipedia as “an umbrella term used to describe a
property of the mind that encompasses many related abilities, such as the capacities to rea-
son, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language,
and to learn. In some cases, intelligence may include traits such as creativity, personal-
ity, character, knowledge, or wisdom”. In our context, we do not include the traits while
referring to intelligent software control in a CR.
6
1.1 Motivation

Traditional research work in the domain of cognitive radio focuses on designing effi-
cient and accurate spectrum sensing techniques as well as defining algorithms for better
spectrum sharing of licensed spectrum among the SUs. Currently, there does not exist
an accurate time-varying spectrum occupancy model for dynamic spectrum access (DSA)
that could be used by wireless researchers in evaluating new algorithms and techniques de-
signed. Chapter 2 primarily covers the representation of a spectrum occupancy model by
probabilistic distribution functions. To validate this model, a qualitative analysis is made
with respect to the real-time measurements obtained from the paging and television bands.
These measurements are recently taken while conducting experiments at the Worcester
Polytechnic Institute, MA. The innovative spectrum occupancy model accomplishes spec-
trum occupancy analysis, one of the important functions of CR as indicated in Figure 1.4.
A plethora of measurement data on spectrum occupancy is readily available while very
little has been undertaken to exploit the information retrieved from these measurements in
designing efficient spectrum sensing techniques. The probabilistic analysis carried out in
Chapter 3 provides valuable qualitative and quantitative information about the spectrum
occupancy. This information is useful in selecting an appropriate section of the spectrum
before proceeding with spectrum sensing techniques. This procedure is proposed a term
called spectrum selection in this dissertation. This is the second vital function of CR shown
in Figure 1.4.
The adaptive spectrum sensing as one of the CR function shown in Figure 1.4 implies
that the spectrum sensing is performed selectively using a-priori data information obtained
from a reliable source. Existing spectrum sensing techniques primarily focus on reducing
the persisting probability of mis-detection (PMD) and probability of false alarm (PFA).
PMD is defined as the probability of failure in detecting an occupied sub-band and PFA is
defined as the probability of detecting a section of a spectrum as occupied while is actu-
ally free. From the network layer perspective, a spectrum sensing technique should also be
capable of retrieving the appropriate spectrum within minimum time duration. The word
“appropriate” accommodates those sections of a spectrum which satisfies the number of
requesting applications and their associated QoS. This leads to a time and spectral effi-
7

Figure 1.4: Various functionalities of a CR.
cient spectrum sensing. Existing research work assumes the prevalence of Markov chain in
spectrum occupancy by licensed primary users. The work presented in this dissertation is
essentially the first initiative in proving such an existence in Chapter 4. Real-time measure-
ments in the paging band have been used in the process of validation. Later in the chapter,
a time and spectral efficient sensing technique has been developed by using concepts from
the Hidden Markov models.
Once the spectrum is sensed and idle sub-bands detected, the final function of a CR
shown in Figure 1.4, is to allocate these sub-bands among the requesting unlicensed sec-
ondary users. This approach refers to spectrum sharing. The problem of spectrum alloca-
tion is dealt with in this dissertation in three different scenarios: (i) Cooperative communi-
cation is studied in CR networks while achieving maximum channel capacity using game
theoretic and Nash equilibrium strategies in Chapter 5, (ii) Scheduling of sub-bands using
a multiple access scheme namely, non-contiguous orthogonal frequency division multiple
access (NC-OFDMA) in Chapter 6, and (iii) Cross-layer architectural design with multi-
objective optimization of sub-band and power allocation in Chapter 7.
1.2 Organization of the Thesis
The rest of the thesis is organized into six chapters as follows:
8
1.2.1 Chapter 2: A Framework for Statistical Wireless Spectrum Oc-
cupancy Modeling
In this chapter, a novel spectrum occupancy model is designed in order to accurately
generate both the temporal and frequency behavior of various wireless transmissions. Us-
ing statistical characteristics from actual radio frequency measurements, first- and second-
order parameters are obtained and employed in a statistical spectrum occupancy model
based on a combination of several different probability density functions (PDFs). In order
to assess the accuracy of the model, output characteristics of proposed spectrum occupancy
model are compared with actual radio frequency measurements.
1.2.2 Chapter 3: Probabilistic Approach to Spectrum Occupancy
In a cognitive radio network, sub-bands of a spectrum are shared by licensed (primary)

and unlicensed (secondary) users in that preferential order. It is generally recognized that
the spectral occupancy by primary users exhibit dynamic spatial and temporal properties
and hence it is a fundamental issue to characterize the spectrum occupancy in terms of
probability. With the sub-band free probabilities being available, an analytical model is
proposed for spectrum occupancy in a cognitive network. To reduce the computational
complexity of the actual distribution of total number of free sub-bands, we resort to efficient
approximation techniques. Furthermore, we characterize free sub-bands into five different
types, based on the occupancy of its adjacent sub-bands. The probability distribution of
total number of each type of sub-bands is then determined. Two corresponding algorithms
are effectively developed to compute different distributions and extensive simulation results
show usefulness of the proposed probabilistic approach.
1.2.3 Chapter 4: Hidden Markov Model in Spectrum Sensing
Design of an efficient spectrum sensing scheme is a challenging task, especially when
false alarms and mis-detections are present. The status of the sub-band is to be monitored
over a sequence of consecutive time periods to determine if at any time point it is either
free or busy. The status of the sub-band over time is proved to evolve randomly, following
9
a Markov chain. The cognitive radio assesses the sub-band, whether or not it is free, and
the assessment is prone to errors. The errors are modeled probabilistically and the entire
edifice is brought under a hidden Markov chain model in predicting the actual sub-band oc-
cupancy. Efficiency of our prediction method in identifying the true states of the sub-band
is substantiated using simulations where Viterbi and Expectation Maximization algorithms
are carried our for reducing the computational complexity.
1.2.4 Chapter 5: Game Theoretic Approach in Spectrum Sharing
In this chapter, we make a unique endeavor in computing channel capacity enhance-
ment of licensed spectrum when the cognitive unlicensed users coexist with the licensed
users using cooperative communication. We illustrate the probabilistic variations of idle
durations, also called white spaces, and their dependence on the location of primary users.
Then, we focus on the central idea of increasing the channel capacity by utilizing the white
spaces for unlicensed users by allowing them to coexist within strict spectral power limits.

We discuss strategies for allocating white spaces among the cognitive secondary users and
seek to optimize the spectrum capacity. We introduce two cooperative N-person games
among the N cognitive users in a Cognitive Radio Network (CRN) and then identify strate-
gies that help achieve Nash equilibria. When licensed users arrive in any of those sub-bands
currently being used by unlincensed users, they need to remove them out of the N-person
game and assess their optional strategies with the licensed users using the 2-person game
approach for coexistence.
1.2.5 Chapter 6: Priority-based Spectrum Allocation in Cognitive Ra-
dio Networks Employing NC-OFDM Transmission
In this chapter, we present three novel priority-based spectrum allocation techniques
for enabling dynamic spectrum access (DSA) networks employing non-contiguous orthog-
onal frequency division multiplexing (NC-OFDM) transmission. The proposed techniques
employs the novel results obtained from the spectrum occupancy statistics, illustrated in
Chapter 2, in deciding the priorities for the spectrum allocations. Each sub-band in the tar-